CN112347992A - Desert region time sequence AGB remote sensing estimation method - Google Patents
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Abstract
The invention discloses a desert area time sequence AGB remote sensing estimation method, which combines a mode of combining Landsat-TM images with high spatial resolution and MODIS images with high temporal resolution and combines the advantages of the two remote sensing images to provide the desert area time sequence AGB remote sensing estimation method which can give consideration to both temporal resolution and spatial resolution.
Description
Technical Field
The invention relates to the technical field of AGB remote sensing estimation, in particular to a desert time sequence AGB remote sensing estimation method combining Landsat-TM images and MODIS images.
Background
The traditional AGB (aboveground biomass) measurement is mainly carried out by adopting a sample plot checking mode, and the mode has the defects of small sample area, poor representativeness, time and labor waste and the like in the actual operation process. The remote sensing technology makes up for the defects of the traditional AGB measuring method to a certain extent, has the advantages of macroscopic property, dynamic property, economical efficiency and the like, and is widely applied to the aspect of AGB estimation.
AGB estimation based on a remote sensing technology is mainly implemented by constructing a Vegetation Index (Vegetation Index, VI) by utilizing linear or nonlinear combination of near infrared emissivity and red light emissivity according to the difference of the reflectivity of Vegetation in near infrared and red light wave bands. At present, tens of vegetation indexes are developed by remote sensing technology, wherein the vegetation indexes are more widely applied, such as normalized vegetation index NDVI, ratio vegetation index RVI, difference vegetation index DVI, soil correction vegetation index SAVI and improved soil correction vegetation index MSAVI. Among the numerous vegetation indices, NDVI is most widely used in current ecological assessment.
Currently, AGB remote sensing estimation at home and abroad mainly aims at ecosystems such as forests, grasslands and the like, and relatively little AGB remote sensing estimation of desert ecosystems is developed. The AGB dynamic monitoring of vegetation in the desert area can reflect the vegetation growth condition, and has important significance for scientifically evaluating the ecological restoration effect of the desert area, researching the carbon cycle process of the desert ecosystem and realizing the healthy development of the desert ecosystem by a production and management decision department.
Vegetation of desert ecosystems is relatively sparse, and the AGB estimation can meet the requirement of time resolution by adopting low-spatial resolution remote sensing images (such as AVHRR and MODIS), but is insufficient in terms of spatial resolution (1km × 1 km). Desert ecosystems have a wide area, and there is an economic disadvantage in using high spatial resolution images (e.g., SPOT, IKNOS, QuickBird, etc.) for AGB estimation. If the Landsat-TM image with the spatial resolution of 30m multiplied by 30m is adopted, the economy and the spatial resolution can be better considered, but the time resolution influenced by the Landsat-TM is 16d, and the remote sensing estimation requirement of the desert ecosystem time sequence AGB cannot be better met.
Disclosure of Invention
Aiming at the defects of the existing desert area AGB remote sensing estimation, the invention provides a desert area time sequence AGB remote sensing estimation method which can give consideration to both time resolution and spatial resolution.
A desert region time sequence AGB remote sensing estimation method comprises the following steps:
step 1, collecting a ground actual measurement AGB sample of a certain desert area, and obtaining NDVI inverted by a contemporaneous Landsat-TM imageTMNDVI of data and contemporaneous MODIS image inversionMODISData;
step 2, through NDVIMODISCalculating vegetation coverage FVC according to data, dividing the desert area into a plurality of areas according to the sequence of the vegetation coverage FVC, and extracting a certain amount of NDVI from each areaTMData and NDVIMODISData;
step 3, NDVITMSpatial resolution interpolation of data to NDVIMODISThe spatial resolution of the data is consistent, and then the NDVI for each regionTMData and NDVIMODISCarrying out straight line fitting on the data to construct the regional NDVITM-NDVIMODISA linear transformation equation;
step 4, combining the AGB sample actually measured on the ground of the desert area and NDVI inverted by the same-period Landsat-TM imageTMData, constructing an AGB _ NDVI unary linear regression model;
step 5, adopting a maximum synthesis method to synthesize MOD13Q1(DOY:177-257) vegetation index data products in the AGB sample time period of the desert region, and then carrying out MODIS image NDVIMODISTo Landsat-TM image NDVITMObtaining NDVI 'of the time-series MODIS images'TM；
Step 6, NDVI 'of the time sequence MODIS images'TMSubstituting the AGB _ NDVI unitary linear regression model to realize the time sequence AGB remote sensing estimation in the desert area.
Further, step 2, dividing the desert area into 5 areas by taking the step length of vegetation coverage FVC increase of 0.2; randomly extracting 300 NDVI (normalized difference magnitudes) in each regionTMData and NDVIMODISData, construction of NDVITM-NDVIMODISA spatial transformation equation.
Further, step 4 is to filter the contemporaneous Landsat-TM image data and then extract the NDVITMData, and combining the AGB sample and NDVI actually measured on the ground in the desert regionTMConstructing an AGB _ NDVI unary linear regression model by using the data; filtering process using lowPass filtering, median filtering or high-pass filtering, using a 3 × 3, 5 × 5 or 7 × 7 filtering kernel; the filtering process is based on NDVI before and after filteringTMDetermining correlation between data and measured AGB samples, selecting filtered NDVITMAnd a filtering mode for improving the correlation between the data and the measured AGB sample.
The method combines the Landsat-TM image with high spatial resolution and the MODIS image with high temporal resolution, combines the advantages of two remote sensing images, provides the desert area time sequence AGB remote sensing estimation method which can give consideration to both temporal resolution and spatial resolution, has the advantages of high precision and continuous estimation time, provides a new remote sensing estimation technology for objectively evaluating the carbon sink change of a desert ecosystem, and provides estimation method support for comprehensively evaluating the terrestrial carbon cycle.
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FIG. 1 is a flow chart of the method for estimating the time sequence AGB remote sensing in the desert area disclosed by the invention;
FIG. 2 is a Gaussian low pass filtered NDVI and AGB spatial scatter plot
FIG. 3 is a Gaussian low pass filtered NDVI and AGB spatial scatter plot.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. The embodiments of the present invention have been presented for purposes of illustration and description, and are not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.
Example 1
In remote AGB estimation, Landsat-TM images have the advantage of high spatial resolution (30m), but have the disadvantages of long satellite revisit period (16d), small scan width (185km), weather-related imaging, and the like. In practical application, problems of inconsistent imaging time of images in the same period, incapability of ensuring image quality and the like are easily caused, and the method cannot be well suitable for AGB remote sensing estimation of a long-time sequence large-area.
The MODIS sensor has the outstanding advantages of high revisiting period (at least 2times/d), large scanning width (2330km), moderate spatial resolution (250m, 500m and 1000m), free supply of land standard data products and the like, and can make up for the defects of TM images in long-time sequence large-area AGB remote sensing estimation to a certain extent.
Because the vegetation indexes obtained by the TM sensor and the MODIS sensor have the difference of spectral resolution and spatial resolution, the AGB remote sensing estimation result of the TM is popularized to the MODIS scale level, and the spatial scale conversion is required.
The core of the method for estimating the time sequence AGB remote sensing in the desert area disclosed by the invention comprises NDVI scale conversion between a TM image and an MODIS image and construction of an AGB-NDVI model.
Taking maocura sand in semiarid area of northwest of china as an example, the existing data adopted comprises 239 actually measured AGB samples of the ground, the contemporary Landsat-TM image (spatial resolution 30m × 30m) and the contemporary MODIS image (spatial resolution 250m × 250m) in the desert area in 8 middle of 2007.
NDVI scale conversion between the existing TM image and MODIS image in the desert region (MODIS → TM)
1. Obtaining NDVI from the Landsat-TM imageTMData, obtaining NDVI according to MODIS imageMODISAnd (4) data.
2. By NDVIMODISData calculation vegetation coverage FVC ═ (NDVI-NDVI)min)/(NDVImax－NDVImin) Wherein NDVImaxAnd NDVIminRespectively representing the maximum vegetation index and the minimum vegetation index of the research area.
3. Dividing the desert area into 5 areas by taking the step length of the vegetation coverage FVC increase of 0.2 as the step length, wherein the area is a 1-level area with the FVC more than or equal to 0 and less than 0.2, a 2-level area with the FVC more than or equal to 0.2 and less than 0.4, a 3-level area with the FVC more than or equal to 0.4 and less than 0.6, a 4-level area with the FVC more than or equal to 0.6 and less than 0.8, and a 5-level area with the FVC more than or equal to 0.8 and less than or.
4. Extracting 300 NDVI from each regionTMData and NDVIMODISAnd (4) data.
5. NDVITMSpatial resolution interpolation of data 30 mx 30m to NDVIMODISThe spatial resolution of the data is consistent at 250m x 250m, where nearest neighbor interpolation may be used.
6. NDVI for each zoneTMData and NDVIMODISCarrying out straight line fitting on the data to construct the regional NDVITM-NDVIMODISThe linear transformation equation, the concrete equation and its parameters are as follows:
NDVITM＝3.0931×NDVIMODIS+0.0514(R2＝0.8181)and 0≤FVC＜0.2
NDVITM＝1.6797×NDVIMODIS－0.1315(R2＝0.8028)and 0.2≤FVC＜0.4
NDVITM＝1.5407×NDVIMODIS－0.1760(R2＝0.8037)and 0.4≤FVC＜0.6
NDVITM＝2.5772×NDVIMODIS－0.7843(R2＝0.8185)and 0.6≤FVC＜0.8
NDVITM＝2.1051×NDVIMODIS－0.7929(R2＝0.8024)and 0.8≤FVC≤1
secondly, the construction of the AGB-NDVI model in the desert region (TM → AGB)
1. Filtering the synchronous Landsat-TM image
The remote sensing image filtering processing can well reduce image noise to a certain extent, and the common spatial filtering processing of the image mainly comprises a low-pass filtering LPF, a median filtering MF and a high-pass filtering HPF. The low-pass filtering can enhance certain frequencies of the image, change the gray difference between ground objects and the neighborhood, and filter high-frequency information in the image, blur the edge of the image and sharp noise. The Gaussian low-pass filtering can filter out the gray scale deviation caused by isolated single-point noise and inhibit the ringing phenomenon of the image. The median filtering can suppress noise (particularly impulse noise) and protect edge characteristics, the median value is endowed with a new value at the center of a filtering kernel, and the effect is obvious in random signal processing. The high-pass filtering realizes noise removal by filtering low-frequency parts in an image in image processing. The filtering has a smoothing effect, and can smooth the image, reduce the spatial heterogeneity of the image and reduce the influence of positioning errors.
Spatial filtering is typically performed by moving a moving window over the original image using spatial convolution techniques in the spatial domain through a filter to perform local operations, and by creating a moving window comprising a matrix of coefficients or weighting factors, typically of odd number of pixels, e.g. 3 × 3, 5 × 5, 7 × 7, etc.
The desert ecosystem has more bare land, the image noise is mainly represented as strong high-frequency noise, 3 filtering modes of Gaussian low-pass filtering, low-pass filtering and median filtering are respectively adopted for the Landsat-TM image, and calculation is carried out according to the filtering windows of 3 multiplied by 3, 5 multiplied by 5 and 7 multiplied by 7. NDVI with 3 × 3, 5 × 5, 7 × 7 filtering kernel Gaussian low pass filteringTMData are represented as NDVI _ GLPF3, NDVI _ GLPF5, and NDVI _ GLPF7, respectively; NDVI with 3 × 3, 5 × 5, 7 × 7 filter kernel low pass filter processingTMData are represented as NDVI _ LPF3, NDVI _ LPF5, and NDVI _ LPF7, NDVI processed with 3 × 3, 5 × 5, 7 × 7 filtering kernel median filtering, respectivelyTMThe data are represented as NDVI _ MF3, NDVI _ MF5, and NDVI _ MF7, respectively.
The correlation between NDVI data after different filtering modes and filtering kernel processing and an actually measured AGB sample is analyzed, and the result shows that the NDVI after different filtering kernel filtering processing is performed under three filtering modes of GLPF, LPF and MFTMData were all significantly correlated with the measured AGB samples at the p < 0.01 level.
The best correlation between the GLPF, LPF and MF filtering results, which represent a 3 × 3 filtering kernel in terms of the filtering kernel scale, and the measured AGB samples is found, which determines the coefficient R20.5010, 0.4951, and 0.4123, respectively; at 5 × 5 and 7 × 7 filter kernel levels, as the filter kernel scale increases, the NDVITMThe filtering smoothing result inhibits the difference of vegetation information space to a certain extent, so that the correlation between the filtering result and the actually measured AGB sample is relatively reduced along with the increase of the filtering kernel scale.
Among three filtering modes of GLPF, LPF and MF, the NDVI _ GLPF3 has the best correlation with the actually measured AGB sample under the condition of 3 multiplied by 3 filtering kernel, and the coefficient (R) is judged2＝0.501)A decision coefficient (R) higher than LPF, MF and NDVI without filtering20.4943). Fig. 2 and 3 are a spatial scatter diagram of NDVI and AGB before gaussian low-pass filtering and a spatial scatter diagram of NDVI and AGB after gaussian low-pass filtering, respectively.
2. According to the correlation analysis result, the NDVI _ GLPF3 data processed by gaussian low-pass filtering by 3 × 3 is selected to perform remote sensing estimation modeling of the time-sequence AGB in the desert region, and 214 randomly measured ground AGBs are used to construct an AGB _ NDVI unary linear regression model AGB of 454.25 × NDVI-42.603 (R is equal to R)2＝0.5029,p<0.01)。
3. Model error analysis is carried out on the constructed AGB _ NDVI unary linear regression model through the remaining 25 random AGB test samples, and the result shows that the average absolute error MAE of the AGB _ NDVI unary linear regression model is 11.216g/m2The average relative error MRE is 13.88%, that is, the AGB-NDVI unitary linear regression model can better realize the AGB estimation in the 8 th-month middle time period of the desert region.
Third, the remote sensing estimation of the time sequence AGB of the desert region (MODIS → AGB)
1. And (3) carrying out error verification generated by estimating AGB by using scale conversion between TM and MODIS, randomly selecting 5 750m × 750m sample squares (S1-S5) according to the vegetation coverage difference from south to north in the desert area, respectively corresponding to 9 × 9 pixel MODIS images and 625 × 625 pixel Landsat-TM images, and carrying out error analysis on the mean value of the sample squares. The TM image was subjected to error analysis as a "true value" in view of spatial resolution, and the results are shown in table 1.
TABLE 1
As can be seen from Table 1, the average absolute error MAR and the average relative error MRE of AGB estimated based on MODIS image and AGB estimated based on Landst-TM image are 2, respectively.86g/m2And 9.99% with an average accuracy of 90.01%. Therefore, the TM-MODIS scale conversion provided by the invention can better realize the remote sensing estimation of the time sequence AGB in the desert region, and has higher estimation precision.
2. Adopting a maximum synthesis method to synthesize MOD13Q1(DOY:177-257) vegetation index data products of 7-9 months per year in the desert region, and then carrying out MODIS image NDVIMODISTo Landsat-TM image NDVITMObtaining NDVI 'of the time-series MODIS images'TM；
3. NDVI 'of time-series MODIS images'TMSubstituting the AGB _ NDVI unitary linear regression model to realize the time sequence AGB remote sensing estimation in the desert area.
It is to be understood that the described embodiments are merely a few embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by one of ordinary skill in the art and related arts based on the embodiments of the present invention without any creative effort, shall fall within the protection scope of the present invention.
Claims (5)
1. A desert region time sequence AGB remote sensing estimation method is characterized by comprising the following steps:
step 1, collecting a ground actual measurement AGB sample in a certain desert area, and obtaining a N DVI with the same period Landsat-TM image inversionTMNDVI of data and contemporaneous MODIS image inversionMODISData;
step 2, through NDVIMODISCalculating vegetation coverage FVC according to data, dividing the desert area into a plurality of areas according to the sequence of the vegetation coverage FVC, and extracting a certain amount of NDVI from each areaTMData and NDVIMODISData;
step 3, NDVITMSpatial resolution interpolation of data to NDVIMODISThe spatial resolution of the data is consistent, and then the NDVI for each regionTMData and NDVIMODISCarrying out straight line fitting on the data to construct the regional NDVITM-N DVIMODISA linear transformation equation;
step (ii) of4, combining the NDVI of the inversion of the AGB sample actually measured on the ground of the desert region and the Landsat-TM image in the same periodTMData, constructing an AGB _ NDVI unary linear regression model;
step 5, adopting a maximum synthesis method to synthesize MOD13Q1(DOY:177-257) vegetation index data products in the AGB sample time period of the desert region, and then carrying out MODIS image NDVIMODISTo Landsat-TM image NDVITMObtaining NDVI 'of the time-series MODIS images'TM；
Step 6, NDVI 'of the time sequence MODIS images'TMSubstituting the AGB _ NDVI unitary linear regression model to realize the time sequence AGB remote sensing estimation in the desert area.
2. The method for estimating the time-series AGB remote sensing of the desert area according to the claim 1, wherein the step 2 divides the desert area into 5 areas by taking the step length of 0.2 increase of the vegetation coverage FVC; randomly extracting 300 NDVI (normalized difference magnitudes) in each regionTMData and NDVIMODISData, construction of NDVITM-NDVIMODISA spatial transformation equation.
3. The method for estimating the time sequence AGB remote sensing in the desert area according to claim 1, wherein step 4 comprises filtering the Landsat-TM image data at the same period, and extracting NDVITMData, and combining the AGB sample and NDVI actually measured on the ground in the desert regionTMAnd (3) constructing an AGB _ NDVI unary linear regression model by using the data.
4. The remote sensing estimation method for the time sequence AGB in the desert area according to claim 3, wherein the filtering process adopts low-pass filtering, median filtering or high-pass filtering, and adopts a 3 x 3, 5 x 5 or 7 x 7 filtering kernel.
5. The method for estimating the time-series AGB remote sensing in the desert area according to claim 4, wherein the filtering process is performed according to the NDVI before and after the filteringTMDetermining correlation between data and measured AGB samples, selecting filtered NDVITMData and actual AGB samplesThe correlation between the two is improved.
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